Distributed Mean-Field Density Estimation for Large-Scale Systems

TL;DR

This paper develops a distributed approach for estimating the mean-field density in large-scale systems using PDE filtering and kernel density estimation, enabling decentralized swarm control.

Contribution

It introduces a novel decentralized density filtering method based on PDE filtering and ISS stability, improving scalability for large swarm systems.

Findings

Centralized density filter converges and remains close to the optimal.

Decentralized local density filters also converge and approximate the centralized filter.

Simulation results validate the effectiveness of the proposed methods.

Abstract

This work studies how to estimate the mean-field density of large-scale systems in a distributed manner. Such problems are motivated by the recent swarm control technique that uses mean-field approximations to represent the collective effect of the swarm, wherein the mean-field density (especially its gradient) is usually used in feedback control design. In the first part, we formulate the density estimation problem as a filtering problem of the associated mean-field partial differential equation (PDE), for which we employ kernel density estimation (KDE) to construct noisy observations and use filtering theory of PDE systems to design an optimal (centralized) density filter. It turns out that the covariance operator of observation noise depends on the unknown density. Hence, we use approximations for the covariance operator to obtain a suboptimal density filter, and prove that both the density estimates and their gradient are convergent and remain close to the optimal one using the notion of input-to-state stability (ISS). In the second part, we continue to study how to decentralize the density filter such that each agent can estimate the mean-field density based on only its own position and local information exchange with neighbors. We prove that the local density filter is also convergent and remains close to the centralized one in the sense of ISS. Simulation results suggest that the centralized suboptimal density filter is able to generate convergent density estimates, and the local density filter is able to converge and remain close to the centralized filter.